Dynamic Context-Aware Recommender System for Home Automation Through Synergistic Unsupervised and Supervised Learning Algorithms

Home automation, supported by smart devices and the internet of things, works to enhance household control. However, the reliance on current systems with fixed rules poses challenges, which can be inflexible and anxiety-provoking for users who want control over their smart home devices, limit responsiveness to changing conditions and affect energy efficiency, comfort and security. To address this, the paper proposes a dynamic personalized recommender system that considers the user's current state and contextual preferences to suggest relevant automation services for smart home devices. The system uses an unsupervised algorithm to extract rules from past interactions and supervised algorithms to make recommendations based on those rules. The proposed context-aware recommender system for smart homes achieved a remarkable average accuracy of 86.99%, a recall of 76.06% and a precision of 82.67% on publicly available datasets, surpassing previous studies. It offers users an enhanced quality of life, energy efficiency and cost reduction, while providing service providers with increased engagement and valuable insights.